Landslide susceptibility assessment and zonation using negative sampling strategy: A case study of Bazhong area, Sichuan Province
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摘要:
滑坡易发性评价是滑坡风险管理的重要环节，能够有效指导防灾减灾工作，但滑坡易发性评价精度受到多种因素制约。当前针对斜坡单元的负样本采样优化策略研究相对较少。文章以四川省巴中地区为研究对象，选取高程、相对高差、历年平均降雨等11个影响因子，以优化斜坡单元负样本采样策略建立地理加权回归-随机森林（GWR-RF）耦合模型，并将评估结果与多次全域随机采样策略进行对比。结果表明：（1）全域随机采样会导致易发性评价结果存在较大差异，且评估结果准确率较差，全域随机采样不适用于以斜坡单元为基础的滑坡易发性评价；（2）GWR-RF耦合模型的滑坡易发性评价结果存在空间差异，主要分布于研究区的恩阳区、巴州区、平昌县，以及南江县中—南部，文章提出的GWR-RF耦合模型通过优化负样本取样策略，提升了滑坡易发性评价的精度，可为巴中地区滑坡灾害防治提供科学依据。
Abstract:Landslide susceptibility assessment is a crucial component of landslide risk management, effectively guiding disaster prevention and mitigation efforts. However, the accuracy of landslide susceptibility assessments is constrained by various factors, and current research on optimizing negative sample sampling strategies based on slope units remains relatively limited. This study, focuses on Bazhong City as the research area, incorporates eleven conditioning factors including elevation, relief, and annual average rainfall to develop a geographically weighted regression - random forest (GWR-RF) coupling model. This model optimize the negative sampling strategy by comparing it against traditional random sampling across the entire area. The results indicate the following: (1) Random sampling from the entire area leads to significant disparities in susceptibility assessments, accompanied by a relatively diminished accuracy, rendering it unsuitable for slope unit-based assessments. (2) The coupled GWR-RF model demonstrates spatial variations in landslide susceptibility, predominantly distributing in the Enyang, Bazhou, Pingchang Counties, and the central - southern region of Nanjiang County. The proposed GWR-RF coupled model improves the accuracy of landslide susceptibility assessments by optimizing the negative sample sampling strategy, providing a scientific basis for landslide disaster prevention and mitigation in the Bazhong region.
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表 1 滑坡易发性分区结果
Table 1. Results of landslide susceptibility zoning
模型 易发性等级 分区面积/km2 面积占比/% 分区滑坡数量/个 滑坡数量占比/% 滑坡密度/（个每100 km2） GWR-RF 极低易发 1704.02 13.85 7 0.65 0.41 低易发 1339.37 10.89 16 1.49 1.19 中易发 2231.27 18.13 66 6.15 2.96 高易发 3698.50 30.06 444 41.38 12.00 极高易发 3331.41 27.07 540 50.33 16.21 RS2 极低易发 1380.00 11.22 4 0.37 0.29 低易发 1344.82 10.93 17 1.58 1.26 中易发 2560.44 20.81 88 8.20 3.44 高易发 6959.27 56.56 935 87.14 13.44 极高易发 60.04 0.49 29 2.70 48.30 RS3 极低易发 1101.49 8.95 1 0.09 0.09 低易发 1219.89 9.91 13 1.21 1.07 中易发 1118.98 9.09 23 2.14 2.06 高易发 5489.84 44.62 522 48.65 9.51 极高易发 3374.37 27.42 514 47.90 15.23 RS7 极低易发 1987.32 16.15 8 0.75 0.40 低易发 1800.17 14.63 17 1.58 0.94 中易发 4941.03 40.16 234 21.81 4.74 高易发 3430.03 27.88 733 68.31 21.37 极高易发 146.07 1.19 81 7.55 55.45 
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表 2 模型效果对比
Table 2. Comparative analysis of model performance
模型 评价指标 精确率 召回率 F1分数 准确率 AUC RS1 0.763 0.798 0.744 0.846 0.873 RS2 0.649 0.693 0.825 0.801 0.730 RS3 0.749 0.785 0.669 0.839 0.847 RS4 0.619 0.643 0.661 0.779 0.698 RS5 0.595 0.626 0.670 0.778 0.663 RS6 0.608 0.637 0.671 0.780 0.684 RS7 0.581 0.623 0.679 0.781 0.624 RS8 0.613 0.644 0.682 0.782 0.679 RS9 0.619 0.649 0.785 0.783 0.695 $\overline {{\text{RS}}} $ 0.644±0.066 0.678±0.068 0.715±0.070 0.796±0.027 0.721±0.084 GWR-RF 0.700 0.735 0.773 0.814 0.845
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